We implement our CGL-GAN in PyTorch and use Adam optimizer [1]}. The initial learning rates for the generator and discriminator are \({10^{-4}}\)  and \({10^{-3}}\)  respectively (\({10^{-5}}\)  and \({10^{-4}}\)  for their corresponding backbones). The whole model is trained for 300 epochs with batch size 128 and all learning rates are reduced by a factor of 10 in the last 100 epochs. The weight of \(L_{adv}\)  linearly increases from zero to one after 50-epoch warm up. The weight of \(L_{rec}\)  is kept as one during the whole training. For efficiency, the posters and images are resized as \(240\times 350\)  for models. The hidden dims in transformers are set to 256.
